Accurate Static Branch Prediction by Value Range Propagation

Abstract

The ability to predict at compile time the likelihood of a particular branch being taken provides valuable information for several optimizations, including global instruction scheduling, code layout, function inlining, interprocedural register allocation and many high level optimizations. Previous attempts at static branch prediction have either used simple heuristics, which can be quite inaccurate, or put the burden onto the programmer by using execution profiling data or source code hints. This paper presents a new approach to static branch prediction called value range propagation. This method tracks the weighted value ranges of variables through a program, much like constant propagation. These value ranges may be either numeric or symbolic in nature. Branch prediction is then performed by simply consulting the value range of the appropriate variable. Heuristics are used as a fallback for cases where the value range of the variable cannot be determined statically. In the process, value range propagation subsumes both constant propagation and copy propagation. Experimental results indicate that this approach produces significantly more accurate predictions than the best existing heuristic techniques. The value range propagation method can be implemented over any “factored” dataflow representation with a static single assignment property (such as SSA form or a dependence flow graph where the variables have been renamed to achieve single assignment). Experimental results indicate that the technique maintains the linear runtime behavior of constant propagation experienced in practice. © 1995, ACM. All rights reserved.